from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-15 14:03:54.060968
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 15, Dec, 2021
Time: 14:03:59
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.5091
Nobs: 506.000 HQIC: -47.9660
Log likelihood: 5838.16 FPE: 1.09795e-21
AIC: -48.2609 Det(Omega_mle): 9.20642e-22
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.358429 0.079509 4.508 0.000
L1.Burgenland 0.098269 0.044017 2.233 0.026
L1.Kärnten -0.115454 0.022648 -5.098 0.000
L1.Niederösterreich 0.178672 0.091206 1.959 0.050
L1.Oberösterreich 0.128375 0.092411 1.389 0.165
L1.Salzburg 0.282624 0.047317 5.973 0.000
L1.Steiermark 0.020963 0.061086 0.343 0.731
L1.Tirol 0.107753 0.049348 2.184 0.029
L1.Vorarlberg -0.082613 0.043471 -1.900 0.057
L1.Wien 0.028998 0.083026 0.349 0.727
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.013131 0.175812 0.075 0.940
L1.Burgenland -0.050901 0.097331 -0.523 0.601
L1.Kärnten 0.036252 0.050080 0.724 0.469
L1.Niederösterreich -0.211060 0.201678 -1.047 0.295
L1.Oberösterreich 0.469046 0.204341 2.295 0.022
L1.Salzburg 0.313966 0.104629 3.001 0.003
L1.Steiermark 0.103559 0.135074 0.767 0.443
L1.Tirol 0.311454 0.109119 2.854 0.004
L1.Vorarlberg 0.009455 0.096125 0.098 0.922
L1.Wien 0.015293 0.183588 0.083 0.934
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.220222 0.040456 5.444 0.000
L1.Burgenland 0.090988 0.022397 4.063 0.000
L1.Kärnten -0.004996 0.011524 -0.434 0.665
L1.Niederösterreich 0.223910 0.046408 4.825 0.000
L1.Oberösterreich 0.167840 0.047020 3.570 0.000
L1.Salzburg 0.037320 0.024076 1.550 0.121
L1.Steiermark 0.026878 0.031082 0.865 0.387
L1.Tirol 0.076924 0.025109 3.064 0.002
L1.Vorarlberg 0.055641 0.022119 2.516 0.012
L1.Wien 0.106580 0.042245 2.523 0.012
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.160935 0.039521 4.072 0.000
L1.Burgenland 0.041916 0.021879 1.916 0.055
L1.Kärnten -0.012663 0.011257 -1.125 0.261
L1.Niederösterreich 0.152120 0.045335 3.355 0.001
L1.Oberösterreich 0.344767 0.045934 7.506 0.000
L1.Salzburg 0.100947 0.023520 4.292 0.000
L1.Steiermark 0.108891 0.030363 3.586 0.000
L1.Tirol 0.087241 0.024529 3.557 0.000
L1.Vorarlberg 0.053561 0.021608 2.479 0.013
L1.Wien -0.038232 0.041269 -0.926 0.354
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.152306 0.075792 2.010 0.044
L1.Burgenland -0.039813 0.041959 -0.949 0.343
L1.Kärnten -0.036385 0.021589 -1.685 0.092
L1.Niederösterreich 0.131061 0.086943 1.507 0.132
L1.Oberösterreich 0.188768 0.088091 2.143 0.032
L1.Salzburg 0.256200 0.045106 5.680 0.000
L1.Steiermark 0.075250 0.058230 1.292 0.196
L1.Tirol 0.131147 0.047041 2.788 0.005
L1.Vorarlberg 0.104523 0.041439 2.522 0.012
L1.Wien 0.039872 0.079145 0.504 0.614
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.078890 0.060037 1.314 0.189
L1.Burgenland 0.015904 0.033237 0.478 0.632
L1.Kärnten 0.051168 0.017102 2.992 0.003
L1.Niederösterreich 0.180584 0.068870 2.622 0.009
L1.Oberösterreich 0.337175 0.069779 4.832 0.000
L1.Salzburg 0.050477 0.035729 1.413 0.158
L1.Steiermark -0.005709 0.046126 -0.124 0.902
L1.Tirol 0.125014 0.037262 3.355 0.001
L1.Vorarlberg 0.058981 0.032825 1.797 0.072
L1.Wien 0.109038 0.062692 1.739 0.082
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.167400 0.072816 2.299 0.022
L1.Burgenland 0.011961 0.040311 0.297 0.767
L1.Kärnten -0.060627 0.020742 -2.923 0.003
L1.Niederösterreich -0.111373 0.083529 -1.333 0.182
L1.Oberösterreich 0.232879 0.084631 2.752 0.006
L1.Salzburg 0.038552 0.043334 0.890 0.374
L1.Steiermark 0.263353 0.055943 4.707 0.000
L1.Tirol 0.489273 0.045194 10.826 0.000
L1.Vorarlberg 0.071631 0.039812 1.799 0.072
L1.Wien -0.100313 0.076037 -1.319 0.187
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.140460 0.080577 1.743 0.081
L1.Burgenland -0.012483 0.044608 -0.280 0.780
L1.Kärnten 0.063643 0.022952 2.773 0.006
L1.Niederösterreich 0.173384 0.092432 1.876 0.061
L1.Oberösterreich -0.079244 0.093653 -0.846 0.397
L1.Salzburg 0.223859 0.047953 4.668 0.000
L1.Steiermark 0.134945 0.061907 2.180 0.029
L1.Tirol 0.052582 0.050011 1.051 0.293
L1.Vorarlberg 0.141727 0.044056 3.217 0.001
L1.Wien 0.164786 0.084141 1.958 0.050
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.456453 0.044636 10.226 0.000
L1.Burgenland -0.001006 0.024711 -0.041 0.968
L1.Kärnten -0.013684 0.012715 -1.076 0.282
L1.Niederösterreich 0.179135 0.051203 3.499 0.000
L1.Oberösterreich 0.263057 0.051879 5.071 0.000
L1.Salzburg 0.019540 0.026564 0.736 0.462
L1.Steiermark -0.011324 0.034293 -0.330 0.741
L1.Tirol 0.071367 0.027704 2.576 0.010
L1.Vorarlberg 0.056270 0.024405 2.306 0.021
L1.Wien -0.018203 0.046611 -0.391 0.696
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.028613 0.093636 0.154929 0.140299 0.066607 0.082176 0.014933 0.208871
Kärnten 0.028613 1.000000 -0.034289 0.130805 0.049513 0.074440 0.456317 -0.080016 0.097445
Niederösterreich 0.093636 -0.034289 1.000000 0.280979 0.100127 0.254123 0.051276 0.143860 0.248251
Oberösterreich 0.154929 0.130805 0.280979 1.000000 0.193944 0.284775 0.160602 0.126232 0.185780
Salzburg 0.140299 0.049513 0.100127 0.193944 1.000000 0.120497 0.061629 0.109709 0.067168
Steiermark 0.066607 0.074440 0.254123 0.284775 0.120497 1.000000 0.132690 0.089243 0.007660
Tirol 0.082176 0.456317 0.051276 0.160602 0.061629 0.132690 1.000000 0.064274 0.127521
Vorarlberg 0.014933 -0.080016 0.143860 0.126232 0.109709 0.089243 0.064274 1.000000 -0.009539
Wien 0.208871 0.097445 0.248251 0.185780 0.067168 0.007660 0.127521 -0.009539 1.000000